*: rename, update images

analyze/04_aggregate_all_analyze_data.go

@@ -439,7 +439,7 @@ func (data *analyzeData) aggregateAll(memoryByKeyPath string, totalRequests int6
 	sort.Sort(keyNumAndMemorys(tslice))
 
 	// aggregate memory by number of keys
-	knms := processTimeSeries(tslice, 1000, totalRequests)
+	knms := findRangesMemory(tslice, 1000, totalRequests)
 	ckk1 := dataframe.NewColumn("KEYS")
 	ckk2 := dataframe.NewColumn("MIN-VMRSS-MB")
 	ckk3 := dataframe.NewColumn("AVG-VMRSS-MB")

analyze/find_ranges_memory.go

@@ -16,7 +16,7 @@ package analyze
 
 import "sort"
 
-func processTimeSeries(tslice []keyNumAndMemory, unit int64, totalRequests int64) []keyNumAndMemory {
+func findRangesMemory(tslice []keyNumAndMemory, unit int64, totalRequests int64) []keyNumAndMemory {
 	sort.Sort(keyNumAndMemorys(tslice))
 
 	cumulKeyN := int64(0)
@@ -55,10 +55,16 @@ func processTimeSeries(tslice []keyNumAndMemory, unit int64, totalRequests int64
 	}
 
 	kss := []keyNumAndMemory{}
-	delete(rm, 0)
+	delete(rm, 0) // drop data at beginning
+
 	for k, v := range rm {
 		// make sure to use 'k' as keyNum
-		kss = append(kss, keyNumAndMemory{keyNum: k, minMemoryMB: v.minMemoryMB, avgMemoryMB: v.avgMemoryMB, maxMemoryMB: v.maxMemoryMB})
+		kss = append(kss, keyNumAndMemory{
+			keyNum:      k,
+			minMemoryMB: v.minMemoryMB,
+			avgMemoryMB: v.avgMemoryMB,
+			maxMemoryMB: v.maxMemoryMB,
+		})
 	}
 	sort.Sort(keyNumAndMemorys(kss))
 

analyze/find_ranges_memory_test.go

@@ -19,7 +19,7 @@ import (
 	"testing"
 )
 
-func Test_processTimeSeries(t *testing.T) {
+func Test_findRangesMemory(t *testing.T) {
 	var tslice []keyNumAndMemory
 	for i := int64(0); i < 10; i++ {
 		dp := keyNumAndMemory{
@@ -29,7 +29,7 @@ func Test_processTimeSeries(t *testing.T) {
 		tslice = append(tslice, dp)
 	}
 
-	pss := processTimeSeries(tslice, 20, 555)
+	pss := findRangesMemory(tslice, 20, 555)
 	expexcted := []keyNumAndMemory{
 		{keyNum: 20, avgMemoryMB: 1},
 		{keyNum: 40, avgMemoryMB: 1},

find_ranges_latency.go

@@ -21,7 +21,7 @@ import (
 	"github.com/coreos/dbtester/pkg/report"
 )
 
-// processTimeSeries sorts all data points by its timestamp.
+// findRangesLatency sorts all data points by its timestamp.
 // And then aggregate by the cumulative throughput,
 // in order to map the number of keys to the average latency.
 //
@@ -36,7 +36,7 @@ import (
 // If unis is 1000 and the average throughput per second is 30,000
 // and its average latency is 10ms, it will have 30 data points with
 // latency 10ms.
-func processTimeSeries(tss report.TimeSeries, unit int64, totalRequests int64) keyNumToAvgLatencys {
+func findRangesLatency(tss report.TimeSeries, unit int64, totalRequests int64) keyNumToAvgLatencys {
 	sort.Sort(tss)
 
 	cumulKeyN := int64(0)

find_ranges_latency_test.go

@@ -22,7 +22,7 @@ import (
 	"github.com/coreos/dbtester/pkg/report"
 )
 
-func Test_processTimeSeries(t *testing.T) {
+func Test_findRangesLatency(t *testing.T) {
 	var tslice report.TimeSeries
 	for i := int64(0); i < 10; i++ {
 		dp := report.DataPoint{
@@ -33,7 +33,7 @@ func Test_processTimeSeries(t *testing.T) {
 		tslice = append(tslice, dp)
 	}
 
-	pss := processTimeSeries(tslice, 20, 555)
+	pss := findRangesLatency(tslice, 20, 555)
 	expexcted := []keyNumToAvgLatency{
 		{keyNum: 20, avgLat: 1},
 		{keyNum: 40, avgLat: 1},

stress_report_save_upload.go

@@ -258,7 +258,7 @@ func (cfg *Config) saveDataLatencyThroughputTimeseries(gcfg TestGroup, st report
 	}
 
 	// aggregate latency by the number of keys
-	tss := processTimeSeries(st.TimeSeries, 1000, gcfg.RequestNumber)
+	tss := findRangesLatency(st.TimeSeries, 1000, gcfg.RequestNumber)
 	ctt1 := dataframe.NewColumn("KEYS")
 	ctt2 := dataframe.NewColumn("MIN-LATENCY-MS")
 	ctt3 := dataframe.NewColumn("AVG-LATENCY-MS")